We are close to something acceptable
rm(list=ls())
seed<-1909
# loading & modifying data
library("readr") # to read the data
library("dplyr") # to manipulate data
library("fastDummies") # create dummies
# charts & tables
library("ggplot2") # to create charts
library("patchwork") # to combine charts
library("flextable") # design tables
library("modelsummary") # structure tables
library("kableExtra") # design table
library("estimatr")
library("ggpubr")
# regression & analysis
library("fixest") # high dimensional FE
library("skimr") # skim the data
# machine learning
library("policytree") # policy tree (Athey & Wager, 2021)
library("grf") # causal forest
library("rsample") # data splitting
library("randomForest") # Traditional Random Forests
library("mlr3") # learners
library("mlr3learners") # learners
library("gbm") # Generalized Boosted Regression
library("DoubleML") # Double ML
# load full dataset
df_repl<-read_delim("../data/FARS-data-full-sample.txt",delim = "\t")%>%
filter(year<2004)%>%
select(-starts_with("imp"))
# load small dataset
df_sel<-read_delim("../data/FARS-data-selection-sample.txt",delim = "\t")%>%
filter(year<2004)%>%
select(-starts_with("imp"))
# remove rows with missing cases
df_repl<-df_repl[complete.cases(df_repl), ]
df_sel<-df_sel[complete.cases(df_sel), ]
# print number of obs
print(paste('Number of observations in the data:',nrow(df_repl),' (full sample);',nrow(df_sel), ' (selected/causal sample)'))
## [1] "Number of observations in the data: 38455 (full sample); 10328 (selected/causal sample)"
# Treatment indicators
df_repl<-df_repl%>%mutate(D=case_when(lapshould==1~"LapShoulderSeat",lapbelt==1~"Lapbelt",
childseat==1~"Childseat",TRUE~"NONE"),
D=factor(D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat")),
Dbinary=case_when(lapshould==1~1,lapbelt==1~1,childseat==1~1,TRUE~0),
car_age=year-modelyr)
df_sel <-df_sel %>%mutate(D=case_when(lapshould==1~"LapShoulderSeat",lapbelt==1~"Lapbelt",
childseat==1~"Childseat",TRUE~"NONE"),
D=factor(D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat")),
Dbinary=case_when(lapshould==1~1,lapbelt==1~1,childseat==1~1,TRUE~0),
car_age=year-modelyr)
# Convert categorical to indicators
df_repl<-dummy_cols(df_repl%>%select(-restraint))%>%select(-starts_with("D_"),-crashtm,-crashcar,-age,-vehicles1,-vehicles2)
df_sel<-dummy_cols(df_sel%>%select(-restraint))%>%select(-starts_with("D_"),-crashtm,-crashcar,-age,-vehicles1,-vehicles2)
#df_repl<-df_repl%>%mutate(day=ifelse(crashtm=="1_day",1,0),night=ifelse(crashtm=="2_night",1,0),morn=ifelse(crashtm=="3_morn",1,0))
#df_sel<- df_sel %>%mutate(day=ifelse(crashtm=="1_day",1,0),night=ifelse(crashtm=="2_night",1,0),morn=ifelse(crashtm=="3_morn",1,0))
# Select variables
#df_repl<-df_repl%>%select(splmU55,thoulbs_I,numcrash,weekend,lowviol,highviol,ruralrd,frimp,suv,death,D,Dbinary,modelyr,age,year,car_age)
#df_sel<- df_sel %>%select(splmU55,thoulbs_I,numcrash,weekend,lowviol,highviol,ruralrd,frimp,suv,death,D,Dbinary,modelyr,age,year,car_age)
# Training and test data
set.seed(seed)
df_repl_split <- initial_split(df_repl, prop = .5)
df_repl_train <- training(df_repl_split)
df_repl_test <- testing(df_repl_split)
df_sel_split <- initial_split(df_sel, prop = .5)
df_sel_train <- training(df_sel_split)
df_sel_test <- testing(df_sel_split)
# OVerride settings above
df_repl_train <- df_repl
df_repl_test <- df_repl
df_sel_train <- df_sel
df_sel_test <- df_sel
# X Matrices
X_repl_train<-as.matrix(df_repl_train%>%select(-death,-D,-Dbinary, -childseat,-lapbelt,-lapshould))
X_repl_test<- as.matrix(df_repl_test%>%select(-death,-D,-Dbinary, -childseat,-lapbelt,-lapshould))
X_sel_train<- as.matrix(df_sel_train%>%select(-death,-D,-Dbinary, -childseat,-lapbelt,-lapshould))
X_sel_test<- as.matrix(df_sel_test%>%select(-death,-D,-Dbinary, -childseat,-lapbelt,-lapshould))
X_repl_train_nocontrols<-as.matrix(rep(1,nrow(X_repl_train)))
X_repl_test_nocontrols<- as.matrix(rep(1,nrow(X_repl_test)))
X_sel_train_nocontrols<- as.matrix(rep(1,nrow(X_sel_train)))
X_sel_test_nocontrols<- as.matrix(rep(1,nrow(X_sel_test)))
# D matrices
D_repl_train<-factor(df_repl_train$D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat"))
D_repl_test<-factor(df_repl_train$D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat"))
D_sel_train<-factor(df_sel_train$D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat"))
D_sel_test<-factor(df_sel_train$D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat"))
D_binary_repl_train<-as.matrix(df_repl_train%>%select(Dbinary))
D_binary_repl_test<- as.matrix(df_repl_test%>%select(Dbinary))
D_binary_sel_train<- as.matrix(df_sel_train%>%select(Dbinary))
D_binary_sel_test<- as.matrix(df_sel_test%>%select(Dbinary))
# Y matrices
Y_repl_train<-as.matrix(df_repl_train%>%select(death))
Y_repl_test<- as.matrix(df_repl_test%>%select(death))
Y_sel_train<- as.matrix(df_sel_train%>%select(death))
Y_sel_test<- as.matrix(df_sel_test%>%select(death))
#set.seed(seed)
#Y.forest_het = regression_forest(X=X_sel_train, Y_sel_train)
#Y.hat_het = predict(Y.forest_het )$predictions
#set.seed(seed)
#W.forest_het = regression_forest(X=X_sel_train, D_binary_sel_train)
#W.hat_het = predict(W.forest_het )$predictions
# Estimate forest
set.seed(seed)
cfbinary<- causal_forest(X=X_sel_train, Y=Y_sel_train, W=D_binary_sel_train,tune.parameters = "all")
average_treatment_effect(cfbinary)
## estimate std.err
## -0.044536725 0.007602517
test_calibration(cfbinary)
##
## Best linear fit using forest predictions (on held-out data)
## as well as the mean forest prediction as regressors, along
## with one-sided heteroskedasticity-robust (HC3) SEs:
##
## Estimate Std. Error t value Pr(>t)
## mean.forest.prediction 0.95373 0.12231 7.7979 3.450e-15 ***
## differential.forest.prediction 1.08311 0.28984 3.7370 9.363e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Get importance
importance=variable_importance(cfbinary)
var_imp <- data.frame(importance=importance,names=colnames(X_sel_train))
ggplot(var_imp,aes(x= reorder(names,importance),y=importance))+
geom_bar(stat="identity",fill="#f56c42",color="white")+
theme_minimal()+
theme(axis.text.x = element_text(angle=45,vjust = 1, hjust=1))+
labs(x=" ")+
coord_flip()
## CATE distribution
# get predictions
cate<-data.frame(sample="CATEs",tau=predict(cfbinary)$predictions)
# histogram all
ggplot(cate,aes(x=tau))+
geom_histogram(aes(y=..count../sum(..count..)),bins=100,alpha=0.95, position = "identity",
fill="#f56c42",color="white")+
theme_minimal()+
labs(title=" ",x="Conditional Average Treatment Effect",y="Density")
# Split sample in 5 groups based on cates
df_sel_train["categroup"] <- factor(ntile(predict(cfbinary)$predictions, n=4))
# calculate AIPW for each sub group
estimated_aipw_ate <- lapply(
seq(4), function(w) {
ate <- average_treatment_effect(cfbinary, subset = df_sel_train$categroup == w,method = "AIPW")
})
# Combine in data da frame
estimated_aipw_ate <- data.frame(do.call(rbind, estimated_aipw_ate))
estimated_aipw_ate$Ntile <- as.numeric(rownames(estimated_aipw_ate))
estimated_aipw_ate$type<-"AIPW"
# Mean of CATES
df_sel_train["cate"]<-predict(cfbinary)$predictions
cates<-df_sel_train%>%group_by(categroup)%>%summarise(estimate=mean(cate))%>%rename(Ntile=categroup)%>%mutate(std.err=NA,type="CATE")
dfplot<-rbind(estimated_aipw_ate,cates)
# create plot
ggplot(dfplot,aes(color=type)) +
geom_pointrange(aes(x = Ntile, y = estimate, ymax = estimate + 1.96 * `std.err`, ymin = estimate - 1.96 * `std.err`),
size = 1,
position = position_dodge(width = .5)) +
theme_minimal() +
geom_hline(yintercept=0,linetype="dashed")+
labs(x = "Quartile", y = "AIPW ATE", title = "AIPW ATEs by quartiles of the conditional average treatment effect")
df_sel_train["tau"]<-predict(cfbinary)$predictions
df_sel_train_col<-df_sel_train%>%
group_by(modelyr,splmU55)%>%
summarise(tau=mean(tau))
p1<-ggplot(df_sel_train_col,aes(x=modelyr,y=tau,color=as.factor(splmU55)))+geom_point()+
ylim(-0.125,0)
df_sel_train_col<-df_sel_train%>%
group_by(year,splmU55)%>%
summarise(tau=mean(tau))
p2<-ggplot(df_sel_train_col,aes(x=year,y=tau,color=as.factor(splmU55)))+geom_point()+
ylim(-0.125,0)+labs(y="")+ theme(axis.title.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
df_sel_train_col<-df_sel_train%>%
group_by(thoulbs_I)%>%
summarise(tau=mean(tau))
p3<-ggplot(df_sel_train_col,aes(x=thoulbs_I*1000,y=tau))+geom_point()+
ylim(-0.125,0)+labs(y="")+ theme(axis.title.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
ggarrange(p1, p2, p3, ncol=3, nrow=1, common.legend = TRUE, legend="bottom")
# get predictions
cate<-data.frame(sample="CATEs",splmU55=df_sel_train$splmU55,tau=predict(cfbinary)$predictions)
# histogram all
ggplot(cate,aes(x=tau,fill=as.factor(splmU55),group=splmU55))+
geom_histogram(aes(y=..count../sum(..count..)),bins=100,alpha=0.5, position = "identity",
color="white")+
theme_minimal()+
labs(title=" ",x="Conditional Average Treatment Effect",y="Density")
cfbinary$tuning.output
## Tuning status: default.
## This indicates tuning was attempted. However, we could not find parameters that were expected to perform better than default:
##
## sample.fraction: 0.5
## mtry: 26
## min.node.size: 5
## honesty.fraction: 0.5
## honesty.prune.leaves: TRUE
## alpha: 0.05
## imbalance.penalty: 0
min.node.size<-c(5,50,100,500)
mtry<-c(26,5)
num.trees<-c(2000,200,4000)
honesty.fraction<-c(0.5,0.25,0.75)
alpha<-c(0.05,0.01,0.1)
sample<-c(.99,0.5)
mygrid<-expand.grid(sample,min.node.size,mtry,num.trees,honesty.fraction,alpha)
colnames(mygrid) <-c("sample","min.node.size","mtry","num.trees","honesty.fraction","alpha")
# Add empty columns to grid
mygrid["ATE"]<-NA
mygrid["mean.forest.prediction"]<-NA
mygrid["differential.forest.prediction"]<-NA
catedist<-vector("list",nrow(mygrid))
# loop over grid
for (i in 1:nrow(mygrid)){
# load small dataset
df_sel<-read_delim("../data/FARS-data-selection-sample.txt",delim = "\t")%>%
filter(year<2004)%>%
select(-starts_with("imp"))
# remove rows with missing cases
df_sel<-df_sel[complete.cases(df_sel), ]
# Treatment indicators
df_sel <-df_sel %>%mutate(D=case_when(lapshould==1~"LapShoulderSeat",lapbelt==1~"Lapbelt",
childseat==1~"Childseat",TRUE~"NONE"),
D=factor(D,levels=c("NONE","Lapbelt","LapShoulderSeat","Childseat")),
Dbinary=case_when(lapshould==1~1,lapbelt==1~1,childseat==1~1,TRUE~0),
car_age=year-modelyr)
# Convert categorical to indicators
df_sel<-dummy_cols(df_sel%>%select(-restraint))%>%select(-starts_with("D_"),-crashtm,-crashcar,-age,-vehicles1,-vehicles2)
# Training and test data
set.seed(seed)
df_sel_split <- initial_split(df_sel, prop =as.numeric(mygrid[i,]["sample"]))
df_sel_train <- training(df_sel_split)
# X Matrices
X_sel_train<- as.matrix(df_sel_train%>%select(-death,-D,-Dbinary, -childseat,-lapbelt,-lapshould))
# D matrices
D_binary_sel_train<- as.matrix(df_sel_train%>%select(Dbinary))
# Y matrices
Y_sel_train<- as.matrix(df_sel_train%>%select(death))
# Estimate causal foreswt
cf_facility_grid <- causal_forest(X_sel_train, Y=Y_sel_train, W=D_binary_sel_train,
num.trees = as.numeric(mygrid[i,]["num.trees"]),
min.node.size = as.numeric(mygrid[i,]["min.node.size"]),
honesty.fraction = as.numeric(mygrid[i,]["honesty.fraction"]),
mtry = as.numeric(mygrid[i,]["mtry"]),
alpha = as.numeric(mygrid[i,]["alpha"])
)
# store treatment effect
te<-average_treatment_effect(cf_facility_grid)
mygrid[i,"ATE"]<-te[1]
# store calibration test
tc <- test_calibration(cf_facility_grid)
mygrid[i,"mean.forest.prediction"]<-tc[1,1]
mygrid[i,"differential.forest.prediction"]<-tc[2,1]
# store chart of CATES
# get predictions
cate<-data.frame(sample="CATEs",splmU55=df_sel_train$splmU55,tau=predict(cf_facility_grid)$predictions)%>%
mutate(SPLMU55=ifelse(splmU55==1," 1", "0"))
# histogram all
p1<-ggplot(cate,aes(x=tau,color=as.factor(SPLMU55),fill=as.factor(SPLMU55),group=SPLMU55))+
geom_histogram(aes(y=..count../sum(..count..)),bins=100,alpha=0.75, position = "identity",
size=0.1,color="white")+theme(text=element_text(size=6))+
theme_minimal()+theme(legend.position = "top")+
labs(fill="splmU55",x="cate",y=" ",title=paste("Spec: ",i))
catedist[[i]]<-p1
}
main<-ggplot(mygrid)+geom_point(aes(x=mean.forest.prediction,y=differential.forest.prediction,color=abs(ATE)))+
theme_minimal()+labs(title="BLP omnibus test across specifications" )
inset<-ggplot(mygrid)+geom_point(aes(x=mean.forest.prediction,y=differential.forest.prediction,color=abs(ATE)))+
theme_minimal()+theme(panel.background = element_rect(fill = "white"))+
xlim(0.5,2)+ylim(-0.5,2)+labs(title="Zoomed")
ggpubr::ggarrange(plotlist=list(main,inset),common.legend = TRUE)
ggpubr::ggarrange(plotlist=catedist,common.legend = TRUE,ncol=4)
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# Print results
mygrid<-mygrid%>%mutate(spec=row_number())
knitr::kable(mygrid, format = "html")
| sample | min.node.size | mtry | num.trees | honesty.fraction | alpha | ATE | mean.forest.prediction | differential.forest.prediction | spec |
|---|---|---|---|---|---|---|---|---|---|
| 0.99 | 5 | 26 | 2000 | 0.50 | 0.05 | -0.0464813 | 0.9651446 | 1.0861549 | 1 |
| 0.50 | 5 | 26 | 2000 | 0.50 | 0.05 | -0.0308396 | 1.0110265 | 0.1864084 | 2 |
| 0.99 | 50 | 26 | 2000 | 0.50 | 0.05 | -0.0471709 | 0.9925460 | 1.2628674 | 3 |
| 0.50 | 50 | 26 | 2000 | 0.50 | 0.05 | -0.0317051 | 1.0079092 | -3.0237487 | 4 |
| 0.99 | 100 | 26 | 2000 | 0.50 | 0.05 | -0.0475969 | 0.9992744 | 0.7010298 | 5 |
| 0.50 | 100 | 26 | 2000 | 0.50 | 0.05 | -0.0316616 | 1.0242146 | -5.8002648 | 6 |
| 0.99 | 500 | 26 | 2000 | 0.50 | 0.05 | -0.0478133 | 0.9929117 | -433.9292669 | 7 |
| 0.50 | 500 | 26 | 2000 | 0.50 | 0.05 | -0.0316912 | 0.8857574 | -368.2848681 | 8 |
| 0.99 | 5 | 5 | 2000 | 0.50 | 0.05 | -0.0508424 | 0.9673798 | 1.4877364 | 9 |
| 0.50 | 5 | 5 | 2000 | 0.50 | 0.05 | -0.0361571 | 1.0229115 | -0.5861640 | 10 |
| 0.99 | 50 | 5 | 2000 | 0.50 | 0.05 | -0.0516348 | 0.9848425 | 2.1152427 | 11 |
| 0.50 | 50 | 5 | 2000 | 0.50 | 0.05 | -0.0367762 | 1.0298163 | -6.6067198 | 12 |
| 0.99 | 100 | 5 | 2000 | 0.50 | 0.05 | -0.0519699 | 1.0054799 | 0.5299175 | 13 |
| 0.50 | 100 | 5 | 2000 | 0.50 | 0.05 | -0.0367438 | 1.0609611 | -12.5742744 | 14 |
| 0.99 | 500 | 5 | 2000 | 0.50 | 0.05 | -0.0521864 | 1.0073840 | -449.8473027 | 15 |
| 0.50 | 500 | 5 | 2000 | 0.50 | 0.05 | -0.0367993 | 0.9460832 | -377.1706509 | 16 |
| 0.99 | 5 | 26 | 200 | 0.50 | 0.05 | NaN | 0.9863505 | 0.7576309 | 17 |
| 0.50 | 5 | 26 | 200 | 0.50 | 0.05 | NaN | 1.0075247 | 0.0796272 | 18 |
| 0.99 | 50 | 26 | 200 | 0.50 | 0.05 | NaN | 0.9803761 | 1.1324719 | 19 |
| 0.50 | 50 | 26 | 200 | 0.50 | 0.05 | NaN | 0.9899161 | -3.3510101 | 20 |
| 0.99 | 100 | 26 | 200 | 0.50 | 0.05 | NaN | 1.0150285 | 0.2686670 | 21 |
| 0.50 | 100 | 26 | 200 | 0.50 | 0.05 | NaN | 1.0117567 | -4.8454915 | 22 |
| 0.99 | 500 | 26 | 200 | 0.50 | 0.05 | NaN | 1.0087830 | -45.7419512 | 23 |
| 0.50 | 500 | 26 | 200 | 0.50 | 0.05 | NaN | 1.0249033 | -53.7527808 | 24 |
| 0.99 | 5 | 5 | 200 | 0.50 | 0.05 | -0.0478944 | 0.9746658 | 1.0860108 | 25 |
| 0.50 | 5 | 5 | 200 | 0.50 | 0.05 | -0.0318198 | 0.9948115 | 0.2904979 | 26 |
| 0.99 | 50 | 5 | 200 | 0.50 | 0.05 | -0.0486089 | 0.9935074 | 1.3080155 | 27 |
| 0.50 | 50 | 5 | 200 | 0.50 | 0.05 | -0.0324863 | 0.9961275 | -3.7443315 | 28 |
| 0.99 | 100 | 5 | 200 | 0.50 | 0.05 | -0.0488572 | 0.9977463 | 0.9370923 | 29 |
| 0.50 | 100 | 5 | 200 | 0.50 | 0.05 | -0.0327597 | 0.9954162 | -7.6564455 | 30 |
| 0.99 | 500 | 5 | 200 | 0.50 | 0.05 | -0.0490342 | 1.0117611 | -47.8525997 | 31 |
| 0.50 | 500 | 5 | 200 | 0.50 | 0.05 | -0.0328934 | 0.9691701 | -54.9015019 | 32 |
| 0.99 | 5 | 26 | 4000 | 0.50 | 0.05 | -0.0456485 | 0.9680690 | 1.0669698 | 33 |
| 0.50 | 5 | 26 | 4000 | 0.50 | 0.05 | -0.0297466 | 1.0456251 | -0.0292799 | 34 |
| 0.99 | 50 | 26 | 4000 | 0.50 | 0.05 | -0.0463082 | 0.9900478 | 1.2336988 | 35 |
| 0.50 | 50 | 26 | 4000 | 0.50 | 0.05 | -0.0308900 | 1.0211839 | -3.1240201 | 36 |
| 0.99 | 100 | 26 | 4000 | 0.50 | 0.05 | -0.0467324 | 0.9997036 | 0.7355514 | 37 |
| 0.50 | 100 | 26 | 4000 | 0.50 | 0.05 | -0.0309331 | 1.0305284 | -5.5599991 | 38 |
| 0.99 | 500 | 26 | 4000 | 0.50 | 0.05 | -0.0469811 | 0.9745845 | -692.2509941 | 39 |
| 0.50 | 500 | 26 | 4000 | 0.50 | 0.05 | -0.0309818 | 0.9239964 | -611.2824807 | 40 |
| 0.99 | 5 | 5 | 4000 | 0.50 | 0.05 | -0.0512976 | 0.9661126 | 1.5653321 | 41 |
| 0.50 | 5 | 5 | 4000 | 0.50 | 0.05 | -0.0362162 | 1.0372490 | -0.4630703 | 42 |
| 0.99 | 50 | 5 | 4000 | 0.50 | 0.05 | -0.0520312 | 0.9933655 | 2.2132518 | 43 |
| 0.50 | 50 | 5 | 4000 | 0.50 | 0.05 | -0.0369699 | 1.0246569 | -6.5288471 | 44 |
| 0.99 | 100 | 5 | 4000 | 0.50 | 0.05 | -0.0523189 | 1.0051232 | 0.9569628 | 45 |
| 0.50 | 100 | 5 | 4000 | 0.50 | 0.05 | -0.0369844 | 1.0572589 | -13.6868245 | 46 |
| 0.99 | 500 | 5 | 4000 | 0.50 | 0.05 | -0.0525443 | 0.9700216 | -766.2065157 | 47 |
| 0.50 | 500 | 5 | 4000 | 0.50 | 0.05 | -0.0370494 | 0.9644331 | -615.0671242 | 48 |
| 0.99 | 5 | 26 | 2000 | 0.25 | 0.05 | -0.0469349 | 0.9480296 | 1.3714542 | 49 |
| 0.50 | 5 | 26 | 2000 | 0.25 | 0.05 | -0.0309530 | 1.0534532 | -0.4413483 | 50 |
| 0.99 | 50 | 26 | 2000 | 0.25 | 0.05 | -0.0476211 | 0.9983980 | 0.5257131 | 51 |
| 0.50 | 50 | 26 | 2000 | 0.25 | 0.05 | -0.0315935 | 1.0760074 | -9.0389540 | 52 |
| 0.99 | 100 | 26 | 2000 | 0.25 | 0.05 | -0.0476519 | 1.0020821 | 0.1714081 | 53 |
| 0.50 | 100 | 26 | 2000 | 0.25 | 0.05 | -0.0314651 | 1.1061922 | -8.9717462 | 54 |
| 0.99 | 500 | 26 | 2000 | 0.25 | 0.05 | -0.0478263 | 0.9834969 | -602.1096072 | 55 |
| 0.50 | 500 | 26 | 2000 | 0.25 | 0.05 | -0.0316805 | 0.8160507 | -491.2481891 | 56 |
| 0.99 | 5 | 5 | 2000 | 0.25 | 0.05 | -0.0512502 | 0.9608032 | 1.8448875 | 57 |
| 0.50 | 5 | 5 | 2000 | 0.25 | 0.05 | -0.0362588 | 1.0467467 | -1.4391067 | 58 |
| 0.99 | 50 | 5 | 2000 | 0.25 | 0.05 | -0.0521172 | 0.9978172 | 0.9389714 | 59 |
| 0.50 | 50 | 5 | 2000 | 0.25 | 0.05 | -0.0367577 | 1.0683394 | -18.2630439 | 60 |
| 0.99 | 100 | 5 | 2000 | 0.25 | 0.05 | -0.0521486 | 0.9980434 | -1.1946816 | 61 |
| 0.50 | 100 | 5 | 2000 | 0.25 | 0.05 | -0.0367097 | 1.2620716 | -59.3794729 | 62 |
| 0.99 | 500 | 5 | 2000 | 0.25 | 0.05 | -0.0522301 | 1.0242483 | -610.0932084 | 63 |
| 0.50 | 500 | 5 | 2000 | 0.25 | 0.05 | -0.0368130 | 0.8690996 | -507.5702897 | 64 |
| 0.99 | 5 | 26 | 200 | 0.25 | 0.05 | NaN | 0.9689934 | 0.9446134 | 65 |
| 0.50 | 5 | 26 | 200 | 0.25 | 0.05 | NaN | 0.9822842 | -0.1264885 | 66 |
| 0.99 | 50 | 26 | 200 | 0.25 | 0.05 | NaN | 0.9921335 | 0.7434756 | 67 |
| 0.50 | 50 | 26 | 200 | 0.25 | 0.05 | NaN | 1.0574178 | -6.9280364 | 68 |
| 0.99 | 100 | 26 | 200 | 0.25 | 0.05 | NaN | 1.0030253 | 0.5692985 | 69 |
| 0.50 | 100 | 26 | 200 | 0.25 | 0.05 | NaN | 1.0792015 | -8.3405645 | 70 |
| 0.99 | 500 | 26 | 200 | 0.25 | 0.05 | NaN | 1.0053630 | -72.4644864 | 71 |
| 0.50 | 500 | 26 | 200 | 0.25 | 0.05 | NaN | 1.0140027 | -69.9080198 | 72 |
| 0.99 | 5 | 5 | 200 | 0.25 | 0.05 | -0.0479420 | 0.9667951 | 1.2234862 | 73 |
| 0.50 | 5 | 5 | 200 | 0.25 | 0.05 | -0.0321171 | 1.0067924 | -0.7827403 | 74 |
| 0.99 | 50 | 5 | 200 | 0.25 | 0.05 | -0.0488924 | 1.0011758 | 0.9682092 | 75 |
| 0.50 | 50 | 5 | 200 | 0.25 | 0.05 | -0.0326244 | 1.0047827 | -10.4066037 | 76 |
| 0.99 | 100 | 5 | 200 | 0.25 | 0.05 | -0.0489056 | 1.0040116 | -0.2984342 | 77 |
| 0.50 | 100 | 5 | 200 | 0.25 | 0.05 | -0.0326667 | 1.0931813 | -35.6560117 | 78 |
| 0.99 | 500 | 5 | 200 | 0.25 | 0.05 | -0.0490576 | 1.0249467 | -66.9229479 | 79 |
| 0.50 | 500 | 5 | 200 | 0.25 | 0.05 | -0.0327344 | 1.0144927 | -71.0032667 | 80 |
| 0.99 | 5 | 26 | 4000 | 0.25 | 0.05 | -0.0459634 | 0.9576279 | 1.2895231 | 81 |
| 0.50 | 5 | 26 | 4000 | 0.25 | 0.05 | -0.0300509 | 1.0705153 | -0.6758056 | 82 |
| 0.99 | 50 | 26 | 4000 | 0.25 | 0.05 | -0.0467675 | 1.0028249 | 0.6282334 | 83 |
| 0.50 | 50 | 26 | 4000 | 0.25 | 0.05 | -0.0309008 | 1.0735966 | -9.3392587 | 84 |
| 0.99 | 100 | 26 | 4000 | 0.25 | 0.05 | -0.0468010 | 1.0053597 | 0.2691505 | 85 |
| 0.50 | 100 | 26 | 4000 | 0.25 | 0.05 | -0.0307848 | 1.1003857 | -8.9785572 | 86 |
| 0.99 | 500 | 26 | 4000 | 0.25 | 0.05 | -0.0469675 | 0.9842089 | -961.6023472 | 87 |
| 0.50 | 500 | 26 | 4000 | 0.25 | 0.05 | -0.0309791 | 0.9200156 | -779.8869580 | 88 |
| 0.99 | 5 | 5 | 4000 | 0.25 | 0.05 | -0.0516490 | 0.9705454 | 1.8100402 | 89 |
| 0.50 | 5 | 5 | 4000 | 0.25 | 0.05 | -0.0365337 | 1.0490291 | -1.4750302 | 90 |
| 0.99 | 50 | 5 | 4000 | 0.25 | 0.05 | -0.0524148 | 1.0042273 | 1.0863389 | 91 |
| 0.50 | 50 | 5 | 4000 | 0.25 | 0.05 | -0.0370281 | 1.0559054 | -15.5244930 | 92 |
| 0.99 | 100 | 5 | 4000 | 0.25 | 0.05 | -0.0524818 | 1.0012965 | -0.8789636 | 93 |
| 0.50 | 100 | 5 | 4000 | 0.25 | 0.05 | -0.0369859 | 1.2388672 | -59.8941671 | 94 |
| 0.99 | 500 | 5 | 4000 | 0.25 | 0.05 | -0.0525529 | 0.9765270 | -1010.2179033 | 95 |
| 0.50 | 500 | 5 | 4000 | 0.25 | 0.05 | -0.0370569 | 0.9836482 | -810.6070250 | 96 |
| 0.99 | 5 | 26 | 2000 | 0.75 | 0.05 | -0.0462077 | 0.9735466 | 0.9805956 | 97 |
| 0.50 | 5 | 26 | 2000 | 0.75 | 0.05 | -0.0308959 | 1.0300369 | 0.1756610 | 98 |
| 0.99 | 50 | 26 | 2000 | 0.75 | 0.05 | -0.0469410 | 0.9911058 | 1.0735630 | 99 |
| 0.50 | 50 | 26 | 2000 | 0.75 | 0.05 | -0.0317194 | 0.9789251 | -0.5851855 | 100 |
| 0.99 | 100 | 26 | 2000 | 0.75 | 0.05 | -0.0473469 | 0.9749929 | 1.1976559 | 101 |
| 0.50 | 100 | 26 | 2000 | 0.75 | 0.05 | -0.0318063 | 0.9895082 | -2.3196583 | 102 |
| 0.99 | 500 | 26 | 2000 | 0.75 | 0.05 | -0.0475818 | 0.9965501 | 0.8049715 | 103 |
| 0.50 | 500 | 26 | 2000 | 0.75 | 0.05 | -0.0317588 | 0.8801995 | -211.2353778 | 104 |
| 0.99 | 5 | 5 | 2000 | 0.75 | 0.05 | -0.0507456 | 0.9739822 | 1.2735467 | 105 |
| 0.50 | 5 | 5 | 2000 | 0.75 | 0.05 | -0.0361169 | 1.0011967 | -0.0836522 | 106 |
| 0.99 | 50 | 5 | 2000 | 0.75 | 0.05 | -0.0513718 | 0.9798611 | 1.8236947 | 107 |
| 0.50 | 50 | 5 | 2000 | 0.75 | 0.05 | -0.0369601 | 0.9945110 | -3.1224794 | 108 |
| 0.99 | 100 | 5 | 2000 | 0.75 | 0.05 | -0.0519413 | 0.9880235 | 1.5272715 | 109 |
| 0.50 | 100 | 5 | 2000 | 0.75 | 0.05 | -0.0369215 | 1.0060749 | -8.4989044 | 110 |
| 0.99 | 500 | 5 | 2000 | 0.75 | 0.05 | -0.0522011 | 1.0116582 | -5.6655549 | 111 |
| 0.50 | 500 | 5 | 2000 | 0.75 | 0.05 | -0.0367864 | 0.9345364 | -211.8288954 | 112 |
| 0.99 | 5 | 26 | 200 | 0.75 | 0.05 | NaN | 1.0209706 | 0.5837375 | 113 |
| 0.50 | 5 | 26 | 200 | 0.75 | 0.05 | NaN | 1.0257565 | 0.0656340 | 114 |
| 0.99 | 50 | 26 | 200 | 0.75 | 0.05 | NaN | 0.9691787 | 0.6079613 | 115 |
| 0.50 | 50 | 26 | 200 | 0.75 | 0.05 | NaN | 1.0257969 | -1.1553810 | 116 |
| 0.99 | 100 | 26 | 200 | 0.75 | 0.05 | NaN | 1.0093267 | 0.6623443 | 117 |
| 0.50 | 100 | 26 | 200 | 0.75 | 0.05 | NaN | 1.0414920 | -1.9444367 | 118 |
| 0.99 | 500 | 26 | 200 | 0.75 | 0.05 | NaN | 0.9633947 | 0.4964727 | 119 |
| 0.50 | 500 | 26 | 200 | 0.75 | 0.05 | NaN | 0.9929729 | -31.1203969 | 120 |
| 0.99 | 5 | 5 | 200 | 0.75 | 0.05 | -0.0472041 | 0.9779354 | 0.6264987 | 121 |
| 0.50 | 5 | 5 | 200 | 0.75 | 0.05 | -0.0323505 | 0.9858453 | -0.2065930 | 122 |
| 0.99 | 50 | 5 | 200 | 0.75 | 0.05 | -0.0482907 | 1.0037870 | 0.8903699 | 123 |
| 0.50 | 50 | 5 | 200 | 0.75 | 0.05 | -0.0323374 | 0.9916905 | -1.1762839 | 124 |
| 0.99 | 100 | 5 | 200 | 0.75 | 0.05 | -0.0487127 | 1.0042163 | 0.9029263 | 125 |
| 0.50 | 100 | 5 | 200 | 0.75 | 0.05 | -0.0320895 | 1.0654272 | -3.1493355 | 126 |
| 0.99 | 500 | 5 | 200 | 0.75 | 0.05 | -0.0489311 | 1.0163039 | -3.8511347 | 127 |
| 0.50 | 500 | 5 | 200 | 0.75 | 0.05 | -0.0325696 | 0.9945746 | -28.7482650 | 128 |
| 0.99 | 5 | 26 | 4000 | 0.75 | 0.05 | -0.0451913 | 0.9759214 | 1.0032988 | 129 |
| 0.50 | 5 | 26 | 4000 | 0.75 | 0.05 | -0.0299775 | 1.0423982 | 0.2149985 | 130 |
| 0.99 | 50 | 26 | 4000 | 0.75 | 0.05 | -0.0459983 | 0.9935562 | 1.0885812 | 131 |
| 0.50 | 50 | 26 | 4000 | 0.75 | 0.05 | -0.0308818 | 1.0088231 | -0.7013619 | 132 |
| 0.99 | 100 | 26 | 4000 | 0.75 | 0.05 | -0.0464448 | 0.9869552 | 1.1666723 | 133 |
| 0.50 | 100 | 26 | 4000 | 0.75 | 0.05 | -0.0309769 | 1.0069088 | -2.1611085 | 134 |
| 0.99 | 500 | 26 | 4000 | 0.75 | 0.05 | -0.0466652 | 1.0088561 | 0.7804732 | 135 |
| 0.50 | 500 | 26 | 4000 | 0.75 | 0.05 | -0.0309935 | 0.8700888 | -370.1724149 | 136 |
| 0.99 | 5 | 5 | 4000 | 0.75 | 0.05 | -0.0509310 | 0.9747483 | 1.4669398 | 137 |
| 0.50 | 5 | 5 | 4000 | 0.75 | 0.05 | -0.0361762 | 1.0274434 | -0.1929513 | 138 |
| 0.99 | 50 | 5 | 4000 | 0.75 | 0.05 | -0.0517823 | 0.9830155 | 1.8452205 | 139 |
| 0.50 | 50 | 5 | 4000 | 0.75 | 0.05 | -0.0369891 | 1.0166982 | -3.2209473 | 140 |
| 0.99 | 100 | 5 | 4000 | 0.75 | 0.05 | -0.0521541 | 0.9932945 | 1.6030072 | 141 |
| 0.50 | 100 | 5 | 4000 | 0.75 | 0.05 | -0.0370687 | 1.0089033 | -9.3275027 | 142 |
| 0.99 | 500 | 5 | 4000 | 0.75 | 0.05 | -0.0523806 | 1.0280217 | -6.0836068 | 143 |
| 0.50 | 500 | 5 | 4000 | 0.75 | 0.05 | -0.0369970 | 0.9472866 | -364.0514874 | 144 |
| 0.99 | 5 | 26 | 2000 | 0.50 | 0.01 | -0.0452574 | 0.9672048 | 1.0056546 | 145 |
| 0.50 | 5 | 26 | 2000 | 0.50 | 0.01 | -0.0300843 | 1.0223633 | 0.1419563 | 146 |
| 0.99 | 50 | 26 | 2000 | 0.50 | 0.01 | -0.0460458 | 0.9891263 | 1.2961782 | 147 |
| 0.50 | 50 | 26 | 2000 | 0.50 | 0.01 | -0.0311976 | 1.0147218 | -3.0380347 | 148 |
| 0.99 | 100 | 26 | 2000 | 0.50 | 0.01 | -0.0464950 | 1.0011792 | 0.5645380 | 149 |
| 0.50 | 100 | 26 | 2000 | 0.50 | 0.01 | -0.0312200 | 1.0222695 | -6.4886656 | 150 |
| 0.99 | 500 | 26 | 2000 | 0.50 | 0.01 | -0.0467371 | 0.9996807 | -436.3613370 | 151 |
| 0.50 | 500 | 26 | 2000 | 0.50 | 0.01 | -0.0312322 | 0.8913448 | -368.1235280 | 152 |
| 0.99 | 5 | 5 | 2000 | 0.50 | 0.01 | -0.0499110 | 0.9682434 | 1.4233853 | 153 |
| 0.50 | 5 | 5 | 2000 | 0.50 | 0.01 | -0.0347152 | 1.0200705 | -0.3969075 | 154 |
| 0.99 | 50 | 5 | 2000 | 0.50 | 0.01 | -0.0506066 | 0.9858738 | 2.0734901 | 155 |
| 0.50 | 50 | 5 | 2000 | 0.50 | 0.01 | -0.0354902 | 1.0310887 | -6.2139101 | 156 |
| 0.99 | 100 | 5 | 2000 | 0.50 | 0.01 | -0.0508749 | 1.0059670 | 0.5122674 | 157 |
| 0.50 | 100 | 5 | 2000 | 0.50 | 0.01 | -0.0354865 | 1.0606247 | -12.0770826 | 158 |
| 0.99 | 500 | 5 | 2000 | 0.50 | 0.01 | -0.0510754 | 1.0201945 | -446.3554623 | 159 |
| 0.50 | 500 | 5 | 2000 | 0.50 | 0.01 | -0.0355519 | 0.9490554 | -372.0987062 | 160 |
| 0.99 | 5 | 26 | 200 | 0.50 | 0.01 | NaN | 0.9819781 | 0.8129359 | 161 |
| 0.50 | 5 | 26 | 200 | 0.50 | 0.01 | NaN | 0.9992652 | 0.1992501 | 162 |
| 0.99 | 50 | 26 | 200 | 0.50 | 0.01 | NaN | 0.9621738 | 1.2020637 | 163 |
| 0.50 | 50 | 26 | 200 | 0.50 | 0.01 | NaN | 0.9624404 | -3.3339947 | 164 |
| 0.99 | 100 | 26 | 200 | 0.50 | 0.01 | NaN | 1.0009298 | 0.4146038 | 165 |
| 0.50 | 100 | 26 | 200 | 0.50 | 0.01 | NaN | 0.9839136 | -5.2104870 | 166 |
| 0.99 | 500 | 26 | 200 | 0.50 | 0.01 | NaN | 1.0029231 | -44.9120530 | 167 |
| 0.50 | 500 | 26 | 200 | 0.50 | 0.01 | NaN | 1.0345217 | -55.4231362 | 168 |
| 0.99 | 5 | 5 | 200 | 0.50 | 0.01 | -0.0503708 | 0.9639020 | 1.1687905 | 169 |
| 0.50 | 5 | 5 | 200 | 0.50 | 0.01 | -0.0353172 | 0.9758209 | -0.0128226 | 170 |
| 0.99 | 50 | 5 | 200 | 0.50 | 0.01 | -0.0517468 | 0.9826825 | 1.4989271 | 171 |
| 0.50 | 50 | 5 | 200 | 0.50 | 0.01 | -0.0353427 | 0.9849699 | -4.5098698 | 172 |
| 0.99 | 100 | 5 | 200 | 0.50 | 0.01 | -0.0521235 | 0.9962361 | 0.8208252 | 173 |
| 0.50 | 100 | 5 | 200 | 0.50 | 0.01 | -0.0354782 | 1.0270830 | -9.8302397 | 174 |
| 0.99 | 500 | 5 | 200 | 0.50 | 0.01 | -0.0523620 | 1.0083304 | -49.4819416 | 175 |
| 0.50 | 500 | 5 | 200 | 0.50 | 0.01 | -0.0356572 | 0.9709109 | -53.8776539 | 176 |
| 0.99 | 5 | 26 | 4000 | 0.50 | 0.01 | -0.0447760 | 0.9680096 | 1.0665579 | 177 |
| 0.50 | 5 | 26 | 4000 | 0.50 | 0.01 | -0.0296743 | 1.0414981 | 0.0322577 | 178 |
| 0.99 | 50 | 26 | 4000 | 0.50 | 0.01 | -0.0455066 | 0.9886655 | 1.2653267 | 179 |
| 0.50 | 50 | 26 | 4000 | 0.50 | 0.01 | -0.0306070 | 1.0231000 | -2.9301928 | 180 |
| 0.99 | 100 | 26 | 4000 | 0.50 | 0.01 | -0.0459251 | 0.9976787 | 0.6939413 | 181 |
| 0.50 | 100 | 26 | 4000 | 0.50 | 0.01 | -0.0306385 | 1.0279861 | -4.7345410 | 182 |
| 0.99 | 500 | 26 | 4000 | 0.50 | 0.01 | -0.0461389 | 0.9772375 | -690.8710659 | 183 |
| 0.50 | 500 | 26 | 4000 | 0.50 | 0.01 | -0.0306949 | 0.9237698 | -610.8610965 | 184 |
| 0.99 | 5 | 5 | 4000 | 0.50 | 0.01 | -0.0496099 | 0.9686285 | 1.4370919 | 185 |
| 0.50 | 5 | 5 | 4000 | 0.50 | 0.01 | -0.0343892 | 1.0328349 | -0.4183200 | 186 |
| 0.99 | 50 | 5 | 4000 | 0.50 | 0.01 | -0.0504619 | 0.9913015 | 2.1369926 | 187 |
| 0.50 | 50 | 5 | 4000 | 0.50 | 0.01 | -0.0351429 | 1.0326873 | -7.2561497 | 188 |
| 0.99 | 100 | 5 | 4000 | 0.50 | 0.01 | -0.0507362 | 1.0063444 | 0.6999251 | 189 |
| 0.50 | 100 | 5 | 4000 | 0.50 | 0.01 | -0.0351676 | 1.0660659 | -14.6711396 | 190 |
| 0.99 | 500 | 5 | 4000 | 0.50 | 0.01 | -0.0509572 | 0.9708385 | -754.5135492 | 191 |
| 0.50 | 500 | 5 | 4000 | 0.50 | 0.01 | -0.0352212 | 0.9661965 | -601.4046526 | 192 |
| 0.99 | 5 | 26 | 2000 | 0.25 | 0.01 | -0.0457705 | 0.9518773 | 1.2797429 | 193 |
| 0.50 | 5 | 26 | 2000 | 0.25 | 0.01 | -0.0302919 | 1.0610601 | -0.5825882 | 194 |
| 0.99 | 50 | 26 | 2000 | 0.25 | 0.01 | -0.0465059 | 0.9984947 | 0.5418631 | 195 |
| 0.50 | 50 | 26 | 2000 | 0.25 | 0.01 | -0.0311267 | 1.0796927 | -9.4651112 | 196 |
| 0.99 | 100 | 26 | 2000 | 0.25 | 0.01 | -0.0465378 | 1.0028202 | 0.0866301 | 197 |
| 0.50 | 100 | 26 | 2000 | 0.25 | 0.01 | -0.0310099 | 1.1111169 | -9.2038233 | 198 |
| 0.99 | 500 | 26 | 2000 | 0.25 | 0.01 | -0.0467508 | 0.9904041 | -601.8105302 | 199 |
| 0.50 | 500 | 26 | 2000 | 0.25 | 0.01 | -0.0312212 | 0.8180553 | -488.8410657 | 200 |
| 0.99 | 5 | 5 | 2000 | 0.25 | 0.01 | -0.0502225 | 0.9675655 | 1.6692331 | 201 |
| 0.50 | 5 | 5 | 2000 | 0.25 | 0.01 | -0.0350269 | 1.0367678 | -1.1895390 | 202 |
| 0.99 | 50 | 5 | 2000 | 0.25 | 0.01 | -0.0509970 | 1.0003423 | 0.7329810 | 203 |
| 0.50 | 50 | 5 | 2000 | 0.25 | 0.01 | -0.0355094 | 1.0781783 | -18.9928987 | 204 |
| 0.99 | 100 | 5 | 2000 | 0.25 | 0.01 | -0.0510357 | 0.9982258 | -1.5156431 | 205 |
| 0.50 | 100 | 5 | 2000 | 0.25 | 0.01 | -0.0354656 | 1.2615583 | -57.0150060 | 206 |
| 0.99 | 500 | 5 | 2000 | 0.25 | 0.01 | -0.0511114 | 1.0376643 | -603.9135505 | 207 |
| 0.50 | 500 | 5 | 2000 | 0.25 | 0.01 | -0.0355655 | 0.8668392 | -499.8319049 | 208 |
| 0.99 | 5 | 26 | 200 | 0.25 | 0.01 | NaN | 0.9457833 | 1.1399161 | 209 |
| 0.50 | 5 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0264508 | -0.6405437 | 210 |
| 0.99 | 50 | 26 | 200 | 0.25 | 0.01 | NaN | 0.9820450 | 0.7305966 | 211 |
| 0.50 | 50 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0471418 | -8.3338285 | 212 |
| 0.99 | 100 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0034415 | 0.7806623 | 213 |
| 0.50 | 100 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0858093 | -14.2546675 | 214 |
| 0.99 | 500 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0012462 | -72.3871388 | 215 |
| 0.50 | 500 | 26 | 200 | 0.25 | 0.01 | NaN | 1.0192057 | -70.6196448 | 216 |
| 0.99 | 5 | 5 | 200 | 0.25 | 0.01 | -0.0513126 | 0.9705580 | 1.4334255 | 217 |
| 0.50 | 5 | 5 | 200 | 0.25 | 0.01 | -0.0347343 | 0.9923166 | -0.8059359 | 218 |
| 0.99 | 50 | 5 | 200 | 0.25 | 0.01 | -0.0522742 | 0.9953557 | 1.2197562 | 219 |
| 0.50 | 50 | 5 | 200 | 0.25 | 0.01 | -0.0355320 | 0.9694415 | -11.4084665 | 220 |
| 0.99 | 100 | 5 | 200 | 0.25 | 0.01 | -0.0522326 | 0.9988579 | -0.8292291 | 221 |
| 0.50 | 100 | 5 | 200 | 0.25 | 0.01 | -0.0354884 | 1.0930430 | -34.3598727 | 222 |
| 0.99 | 500 | 5 | 200 | 0.25 | 0.01 | -0.0523828 | 1.0205458 | -66.4732854 | 223 |
| 0.50 | 500 | 5 | 200 | 0.25 | 0.01 | -0.0355405 | 1.0137356 | -70.2343096 | 224 |
| 0.99 | 5 | 26 | 4000 | 0.25 | 0.01 | -0.0452296 | 0.9559875 | 1.3042783 | 225 |
| 0.50 | 5 | 26 | 4000 | 0.25 | 0.01 | -0.0298694 | 1.0649296 | -0.5563597 | 226 |
| 0.99 | 50 | 26 | 4000 | 0.25 | 0.01 | -0.0459568 | 1.0022442 | 0.6099890 | 227 |
| 0.50 | 50 | 26 | 4000 | 0.25 | 0.01 | -0.0305970 | 1.0720367 | -8.3800988 | 228 |
| 0.99 | 100 | 26 | 4000 | 0.25 | 0.01 | -0.0459806 | 1.0057223 | 0.1666361 | 229 |
| 0.50 | 100 | 26 | 4000 | 0.25 | 0.01 | -0.0304959 | 1.1023114 | -8.7845297 | 230 |
| 0.99 | 500 | 26 | 4000 | 0.25 | 0.01 | -0.0461252 | 0.9835417 | -959.1184265 | 231 |
| 0.50 | 500 | 26 | 4000 | 0.25 | 0.01 | -0.0306915 | 0.9144489 | -778.5162845 | 232 |
| 0.99 | 5 | 5 | 4000 | 0.25 | 0.01 | -0.0500120 | 0.9707846 | 1.7263515 | 233 |
| 0.50 | 5 | 5 | 4000 | 0.25 | 0.01 | -0.0347350 | 1.0515601 | -1.5127434 | 234 |
| 0.99 | 50 | 5 | 4000 | 0.25 | 0.01 | -0.0508161 | 1.0045945 | 0.7109832 | 235 |
| 0.50 | 50 | 5 | 4000 | 0.25 | 0.01 | -0.0352126 | 1.0714114 | -16.7241706 | 236 |
| 0.99 | 100 | 5 | 4000 | 0.25 | 0.01 | -0.0508812 | 1.0014268 | -1.6597557 | 237 |
| 0.50 | 100 | 5 | 4000 | 0.25 | 0.01 | -0.0351574 | 1.2776931 | -64.0448954 | 238 |
| 0.99 | 500 | 5 | 4000 | 0.25 | 0.01 | -0.0509623 | 0.9723084 | -987.0483490 | 239 |
| 0.50 | 500 | 5 | 4000 | 0.25 | 0.01 | -0.0352270 | 0.9834048 | -789.6787700 | 240 |
| 0.99 | 5 | 26 | 2000 | 0.75 | 0.01 | -0.0446248 | 0.9580065 | 1.0052274 | 241 |
| 0.50 | 5 | 26 | 2000 | 0.75 | 0.01 | -0.0300416 | 1.0425122 | -0.0207202 | 242 |
| 0.99 | 50 | 26 | 2000 | 0.75 | 0.01 | -0.0458207 | 0.9954044 | 1.1010972 | 243 |
| 0.50 | 50 | 26 | 2000 | 0.75 | 0.01 | -0.0311969 | 0.9873501 | -1.0245163 | 244 |
| 0.99 | 100 | 26 | 2000 | 0.75 | 0.01 | -0.0461652 | 0.9725830 | 1.2215811 | 245 |
| 0.50 | 100 | 26 | 2000 | 0.75 | 0.01 | -0.0313503 | 0.9884844 | -2.0695087 | 246 |
| 0.99 | 500 | 26 | 2000 | 0.75 | 0.01 | -0.0464709 | 0.9963436 | 0.7717374 | 247 |
| 0.50 | 500 | 26 | 2000 | 0.75 | 0.01 | -0.0313030 | 0.8785547 | -211.4401280 | 248 |
| 0.99 | 5 | 5 | 2000 | 0.75 | 0.01 | -0.0496856 | 0.9679679 | 1.2012141 | 249 |
| 0.50 | 5 | 5 | 2000 | 0.75 | 0.01 | -0.0346834 | 1.0145737 | -0.0164864 | 250 |
| 0.99 | 50 | 5 | 2000 | 0.75 | 0.01 | -0.0503849 | 0.9778422 | 1.8835757 | 251 |
| 0.50 | 50 | 5 | 2000 | 0.75 | 0.01 | -0.0356821 | 0.9874831 | -3.1741644 | 252 |
| 0.99 | 100 | 5 | 2000 | 0.75 | 0.01 | -0.0508608 | 0.9895848 | 1.4055120 | 253 |
| 0.50 | 100 | 5 | 2000 | 0.75 | 0.01 | -0.0356816 | 0.9938841 | -9.1779946 | 254 |
| 0.99 | 500 | 5 | 2000 | 0.75 | 0.01 | -0.0510942 | 1.0114891 | -6.1881767 | 255 |
| 0.50 | 500 | 5 | 2000 | 0.75 | 0.01 | -0.0355386 | 0.9417590 | -210.4301177 | 256 |
| 0.99 | 5 | 26 | 200 | 0.75 | 0.01 | NaN | 1.0004447 | 0.5434780 | 257 |
| 0.50 | 5 | 26 | 200 | 0.75 | 0.01 | NaN | 1.0042458 | 0.2701194 | 258 |
| 0.99 | 50 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9756437 | 0.8490687 | 259 |
| 0.50 | 50 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9899676 | -0.5241257 | 260 |
| 0.99 | 100 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9823265 | 0.7621667 | 261 |
| 0.50 | 100 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9911966 | -1.5243338 | 262 |
| 0.99 | 500 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9603427 | 0.6464883 | 263 |
| 0.50 | 500 | 26 | 200 | 0.75 | 0.01 | NaN | 0.9906852 | -32.1267619 | 264 |
| 0.99 | 5 | 5 | 200 | 0.75 | 0.01 | -0.0498590 | 1.0141104 | 0.5946258 | 265 |
| 0.50 | 5 | 5 | 200 | 0.75 | 0.01 | -0.0346912 | 1.0153863 | -0.2932940 | 266 |
| 0.99 | 50 | 5 | 200 | 0.75 | 0.01 | -0.0514202 | 0.9901645 | 0.9621404 | 267 |
| 0.50 | 50 | 5 | 200 | 0.75 | 0.01 | -0.0354436 | 0.9773718 | 0.6470844 | 268 |
| 0.99 | 100 | 5 | 200 | 0.75 | 0.01 | -0.0521207 | 1.0040683 | 1.0968725 | 269 |
| 0.50 | 100 | 5 | 200 | 0.75 | 0.01 | -0.0351353 | 1.0459424 | -3.3732993 | 270 |
| 0.99 | 500 | 5 | 200 | 0.75 | 0.01 | -0.0522891 | 1.0024542 | -5.0009810 | 271 |
| 0.50 | 500 | 5 | 200 | 0.75 | 0.01 | -0.0354585 | 0.9892309 | -28.0322816 | 272 |
| 0.99 | 5 | 26 | 4000 | 0.75 | 0.01 | -0.0445005 | 0.9761487 | 0.9094738 | 273 |
| 0.50 | 5 | 26 | 4000 | 0.75 | 0.01 | -0.0294665 | 1.0342982 | 0.1642599 | 274 |
| 0.99 | 50 | 26 | 4000 | 0.75 | 0.01 | -0.0452188 | 0.9969990 | 1.0776540 | 275 |
| 0.50 | 50 | 26 | 4000 | 0.75 | 0.01 | -0.0305251 | 1.0125608 | -0.6388392 | 276 |
| 0.99 | 100 | 26 | 4000 | 0.75 | 0.01 | -0.0455609 | 0.9884634 | 1.1626478 | 277 |
| 0.50 | 100 | 26 | 4000 | 0.75 | 0.01 | -0.0306757 | 1.0125511 | -1.6999301 | 278 |
| 0.99 | 500 | 26 | 4000 | 0.75 | 0.01 | -0.0458497 | 1.0104722 | 0.6499585 | 279 |
| 0.50 | 500 | 26 | 4000 | 0.75 | 0.01 | -0.0307064 | 0.8685269 | -369.9428390 | 280 |
| 0.99 | 5 | 5 | 4000 | 0.75 | 0.01 | -0.0491966 | 0.9635856 | 1.3542638 | 281 |
| 0.50 | 5 | 5 | 4000 | 0.75 | 0.01 | -0.0344007 | 1.0374176 | -0.2167643 | 282 |
| 0.99 | 50 | 5 | 4000 | 0.75 | 0.01 | -0.0502420 | 0.9831141 | 1.7850021 | 283 |
| 0.50 | 50 | 5 | 4000 | 0.75 | 0.01 | -0.0351898 | 1.0216798 | -3.7034961 | 284 |
| 0.99 | 100 | 5 | 4000 | 0.75 | 0.01 | -0.0505434 | 0.9968132 | 1.4593430 | 285 |
| 0.50 | 100 | 5 | 4000 | 0.75 | 0.01 | -0.0352309 | 1.0222552 | -9.6079928 | 286 |
| 0.99 | 500 | 5 | 4000 | 0.75 | 0.01 | -0.0508140 | 1.0319358 | -7.6287285 | 287 |
| 0.50 | 500 | 5 | 4000 | 0.75 | 0.01 | -0.0351783 | 0.9401451 | -358.9396639 | 288 |
| 0.99 | 5 | 26 | 2000 | 0.50 | 0.10 | -0.0461331 | 0.9564529 | 1.1275763 | 289 |
| 0.50 | 5 | 26 | 2000 | 0.50 | 0.10 | -0.0299727 | 1.0120895 | 0.2985598 | 290 |
| 0.99 | 50 | 26 | 2000 | 0.50 | 0.10 | -0.0467599 | 0.9893860 | 1.3426881 | 291 |
| 0.50 | 50 | 26 | 2000 | 0.50 | 0.10 | -0.0305982 | 1.0041058 | -2.9281819 | 292 |
| 0.99 | 100 | 26 | 2000 | 0.50 | 0.10 | -0.0471867 | 0.9987179 | 0.7825394 | 293 |
| 0.50 | 100 | 26 | 2000 | 0.50 | 0.10 | -0.0305349 | 1.0208568 | -5.6272492 | 294 |
| 0.99 | 500 | 26 | 2000 | 0.50 | 0.10 | -0.0473468 | 0.9937832 | -435.4389631 | 295 |
| 0.50 | 500 | 26 | 2000 | 0.50 | 0.10 | -0.0305440 | 0.8935995 | -369.6636369 | 296 |
| 0.99 | 5 | 5 | 2000 | 0.50 | 0.10 | -0.0518827 | 0.9696016 | 1.6854423 | 297 |
| 0.50 | 5 | 5 | 2000 | 0.50 | 0.10 | -0.0364685 | 1.0301545 | -0.5030529 | 298 |
| 0.99 | 50 | 5 | 2000 | 0.50 | 0.10 | -0.0525768 | 0.9879687 | 2.2272382 | 299 |
| 0.50 | 50 | 5 | 2000 | 0.50 | 0.10 | -0.0373217 | 1.0153324 | -6.0827731 | 300 |
| 0.99 | 100 | 5 | 2000 | 0.50 | 0.10 | -0.0528597 | 1.0066042 | 0.7830996 | 301 |
| 0.50 | 100 | 5 | 2000 | 0.50 | 0.10 | -0.0372870 | 1.0450146 | -11.3707147 | 302 |
| 0.99 | 500 | 5 | 2000 | 0.50 | 0.10 | -0.0530726 | 1.0049947 | -453.4731085 | 303 |
| 0.50 | 500 | 5 | 2000 | 0.50 | 0.10 | -0.0373372 | 0.9448080 | -380.0867512 | 304 |
| 0.99 | 5 | 26 | 200 | 0.50 | 0.10 | NaN | 0.9711852 | 0.8457177 | 305 |
| 0.50 | 5 | 26 | 200 | 0.50 | 0.10 | -0.0358089 | 1.0382993 | -0.1968456 | 306 |
| 0.99 | 50 | 26 | 200 | 0.50 | 0.10 | NaN | 0.9699527 | 1.2655124 | 307 |
| 0.50 | 50 | 26 | 200 | 0.50 | 0.10 | -0.0368776 | 0.9965467 | -3.8352307 | 308 |
| 0.99 | 100 | 26 | 200 | 0.50 | 0.10 | NaN | 1.0090073 | 0.2260322 | 309 |
| 0.50 | 100 | 26 | 200 | 0.50 | 0.10 | -0.0370130 | 1.0215058 | -6.4471757 | 310 |
| 0.99 | 500 | 26 | 200 | 0.50 | 0.10 | NaN | 1.0039504 | -45.6711477 | 311 |
| 0.50 | 500 | 26 | 200 | 0.50 | 0.10 | -0.0370860 | 1.0295189 | -53.6756861 | 312 |
| 0.99 | 5 | 5 | 200 | 0.50 | 0.10 | -0.0491234 | 0.9668449 | 1.3717664 | 313 |
| 0.50 | 5 | 5 | 200 | 0.50 | 0.10 | -0.0349049 | 0.9999781 | -0.2107462 | 314 |
| 0.99 | 50 | 5 | 200 | 0.50 | 0.10 | -0.0501466 | 0.9774512 | 2.0382243 | 315 |
| 0.50 | 50 | 5 | 200 | 0.50 | 0.10 | -0.0360006 | 0.9956018 | -5.6715625 | 316 |
| 0.99 | 100 | 5 | 200 | 0.50 | 0.10 | -0.0503847 | 1.0134885 | 1.1608924 | 317 |
| 0.50 | 100 | 5 | 200 | 0.50 | 0.10 | -0.0361621 | 1.0251899 | -8.7627231 | 318 |
| 0.99 | 500 | 5 | 200 | 0.50 | 0.10 | -0.0506815 | 1.0087391 | -47.5545257 | 319 |
| 0.50 | 500 | 5 | 200 | 0.50 | 0.10 | -0.0363260 | 0.9612114 | -54.1398721 | 320 |
| 0.99 | 5 | 26 | 4000 | 0.50 | 0.10 | -0.0465443 | 0.9685749 | 1.0209242 | 321 |
| 0.50 | 5 | 26 | 4000 | 0.50 | 0.10 | -0.0293915 | 1.0222477 | 0.1876599 | 322 |
| 0.99 | 50 | 26 | 4000 | 0.50 | 0.10 | -0.0473462 | 0.9856963 | 1.3527017 | 323 |
| 0.50 | 50 | 26 | 4000 | 0.50 | 0.10 | -0.0304185 | 1.0192468 | -2.9315461 | 324 |
| 0.99 | 100 | 26 | 4000 | 0.50 | 0.10 | -0.0477858 | 1.0005463 | 0.7066160 | 325 |
| 0.50 | 100 | 26 | 4000 | 0.50 | 0.10 | -0.0304473 | 1.0279611 | -4.8622542 | 326 |
| 0.99 | 500 | 26 | 4000 | 0.50 | 0.10 | -0.0480896 | 0.9718018 | -692.6978660 | 327 |
| 0.50 | 500 | 26 | 4000 | 0.50 | 0.10 | -0.0304982 | 0.9266351 | -610.8933967 | 328 |
| 0.99 | 5 | 5 | 4000 | 0.50 | 0.10 | -0.0518573 | 0.9637592 | 1.6497084 | 329 |
| 0.50 | 5 | 5 | 4000 | 0.50 | 0.10 | -0.0375229 | 1.0246098 | -0.3185464 | 330 |
| 0.99 | 50 | 5 | 4000 | 0.50 | 0.10 | -0.0525625 | 0.9953631 | 2.3504627 | 331 |
| 0.50 | 50 | 5 | 4000 | 0.50 | 0.10 | -0.0381917 | 1.0182078 | -6.0241842 | 332 |
| 0.99 | 100 | 5 | 4000 | 0.50 | 0.10 | -0.0528176 | 1.0045347 | 1.2222226 | 333 |
| 0.50 | 100 | 5 | 4000 | 0.50 | 0.10 | -0.0382142 | 1.0427839 | -13.0334116 | 334 |
| 0.99 | 500 | 5 | 4000 | 0.50 | 0.10 | -0.0530302 | 0.9656679 | -774.8721831 | 335 |
| 0.50 | 500 | 5 | 4000 | 0.50 | 0.10 | -0.0382738 | 0.9546904 | -625.5478985 | 336 |
| 0.99 | 5 | 26 | 2000 | 0.25 | 0.10 | -0.0465436 | 0.9519367 | 1.3254289 | 337 |
| 0.50 | 5 | 26 | 2000 | 0.25 | 0.10 | -0.0298153 | 1.0413243 | -0.3427730 | 338 |
| 0.99 | 50 | 26 | 2000 | 0.25 | 0.10 | -0.0471978 | 0.9980148 | 0.6283832 | 339 |
| 0.50 | 50 | 26 | 2000 | 0.25 | 0.10 | -0.0304575 | 1.0831659 | -9.7336928 | 340 |
| 0.99 | 100 | 26 | 2000 | 0.25 | 0.10 | -0.0472204 | 1.0009230 | 0.2551567 | 341 |
| 0.50 | 100 | 26 | 2000 | 0.25 | 0.10 | -0.0303311 | 1.1267615 | -11.0770244 | 342 |
| 0.99 | 500 | 26 | 2000 | 0.25 | 0.10 | -0.0473617 | 0.9835375 | -602.2055512 | 343 |
| 0.50 | 500 | 26 | 2000 | 0.25 | 0.10 | -0.0305340 | 0.8224605 | -493.9659628 | 344 |
| 0.99 | 5 | 5 | 2000 | 0.25 | 0.10 | -0.0521880 | 0.9653793 | 1.8815454 | 345 |
| 0.50 | 5 | 5 | 2000 | 0.25 | 0.10 | -0.0369070 | 1.0234523 | -1.1602101 | 346 |
| 0.99 | 50 | 5 | 2000 | 0.25 | 0.10 | -0.0530184 | 1.0002526 | 1.3215400 | 347 |
| 0.50 | 50 | 5 | 2000 | 0.25 | 0.10 | -0.0372817 | 1.0593501 | -16.9115588 | 348 |
| 0.99 | 100 | 5 | 2000 | 0.25 | 0.10 | -0.0530672 | 0.9976684 | -0.3982800 | 349 |
| 0.50 | 100 | 5 | 2000 | 0.25 | 0.10 | -0.0372528 | 1.1721532 | -46.6846740 | 350 |
| 0.99 | 500 | 5 | 2000 | 0.25 | 0.10 | -0.0531197 | 1.0182896 | -618.1224230 | 351 |
| 0.50 | 500 | 5 | 2000 | 0.25 | 0.10 | -0.0373480 | 0.8724661 | -513.1314055 | 352 |
| 0.99 | 5 | 26 | 200 | 0.25 | 0.10 | NaN | 0.9621301 | 0.8942112 | 353 |
| 0.50 | 5 | 26 | 200 | 0.25 | 0.10 | -0.0368818 | 1.0029710 | -0.3969105 | 354 |
| 0.99 | 50 | 26 | 200 | 0.25 | 0.10 | NaN | 0.9835089 | 0.9682266 | 355 |
| 0.50 | 50 | 26 | 200 | 0.25 | 0.10 | -0.0369107 | 1.0243572 | -5.9168629 | 356 |
| 0.99 | 100 | 26 | 200 | 0.25 | 0.10 | NaN | 1.0008601 | 0.7427052 | 357 |
| 0.50 | 100 | 26 | 200 | 0.25 | 0.10 | -0.0369732 | 1.0702649 | -10.7979800 | 358 |
| 0.99 | 500 | 26 | 200 | 0.25 | 0.10 | NaN | 1.0021511 | -72.5163338 | 359 |
| 0.50 | 500 | 26 | 200 | 0.25 | 0.10 | -0.0371091 | 1.0168000 | -69.0675123 | 360 |
| 0.99 | 5 | 5 | 200 | 0.25 | 0.10 | -0.0498333 | 0.9687749 | 1.5798821 | 361 |
| 0.50 | 5 | 5 | 200 | 0.25 | 0.10 | -0.0353539 | 1.0142436 | -1.1482995 | 362 |
| 0.99 | 50 | 5 | 200 | 0.25 | 0.10 | -0.0505523 | 1.0000165 | 1.8913356 | 363 |
| 0.50 | 50 | 5 | 200 | 0.25 | 0.10 | -0.0361044 | 1.0172565 | -13.6600930 | 364 |
| 0.99 | 100 | 5 | 200 | 0.25 | 0.10 | -0.0505858 | 1.0014819 | 0.6457998 | 365 |
| 0.50 | 100 | 5 | 200 | 0.25 | 0.10 | -0.0360709 | 1.1139975 | -32.1613393 | 366 |
| 0.99 | 500 | 5 | 200 | 0.25 | 0.10 | -0.0507058 | 1.0183532 | -66.8614160 | 367 |
| 0.50 | 500 | 5 | 200 | 0.25 | 0.10 | -0.0361600 | 1.0011113 | -70.0727170 | 368 |
| 0.99 | 5 | 26 | 4000 | 0.25 | 0.10 | -0.0469431 | 0.9529883 | 1.3568703 | 369 |
| 0.50 | 5 | 26 | 4000 | 0.25 | 0.10 | -0.0296014 | 1.0534470 | -0.4482278 | 370 |
| 0.99 | 50 | 26 | 4000 | 0.25 | 0.10 | -0.0478362 | 1.0014155 | 0.6547176 | 371 |
| 0.50 | 50 | 26 | 4000 | 0.25 | 0.10 | -0.0304141 | 1.0724605 | -9.2954521 | 372 |
| 0.99 | 100 | 26 | 4000 | 0.25 | 0.10 | -0.0478890 | 1.0055601 | 0.1955712 | 373 |
| 0.50 | 100 | 26 | 4000 | 0.25 | 0.10 | -0.0303131 | 1.1068383 | -9.9019424 | 374 |
| 0.99 | 500 | 26 | 4000 | 0.25 | 0.10 | -0.0480756 | 0.9819231 | -964.5286524 | 375 |
| 0.50 | 500 | 26 | 4000 | 0.25 | 0.10 | -0.0304950 | 0.9227693 | -779.9234720 | 376 |
| 0.99 | 5 | 5 | 4000 | 0.25 | 0.10 | -0.0521542 | 0.9682969 | 1.8704125 | 377 |
| 0.50 | 5 | 5 | 4000 | 0.25 | 0.10 | -0.0377542 | 1.0316873 | -0.9740195 | 378 |
| 0.99 | 50 | 5 | 4000 | 0.25 | 0.10 | -0.0529170 | 1.0047756 | 1.4136220 | 379 |
| 0.50 | 50 | 5 | 4000 | 0.25 | 0.10 | -0.0382536 | 1.0425130 | -14.3786880 | 380 |
| 0.99 | 100 | 5 | 4000 | 0.25 | 0.10 | -0.0529782 | 1.0018594 | -0.2002395 | 381 |
| 0.50 | 100 | 5 | 4000 | 0.25 | 0.10 | -0.0382100 | 1.1949934 | -52.9070685 | 382 |
| 0.99 | 500 | 5 | 4000 | 0.25 | 0.10 | -0.0530377 | 0.9712280 | -1029.6723980 | 383 |
| 0.50 | 500 | 5 | 4000 | 0.25 | 0.10 | -0.0382807 | 0.9782745 | -827.0358504 | 384 |
| 0.99 | 5 | 26 | 2000 | 0.75 | 0.10 | -0.0460615 | 0.9837417 | 0.9597688 | 385 |
| 0.50 | 5 | 26 | 2000 | 0.75 | 0.10 | -0.0299969 | 1.0344986 | 0.1672158 | 386 |
| 0.99 | 50 | 26 | 2000 | 0.75 | 0.10 | -0.0464897 | 0.9901666 | 1.0730010 | 387 |
| 0.50 | 50 | 26 | 2000 | 0.75 | 0.10 | -0.0305563 | 0.9916400 | -0.6845379 | 388 |
| 0.99 | 100 | 26 | 2000 | 0.75 | 0.10 | -0.0469582 | 0.9686237 | 1.2769378 | 389 |
| 0.50 | 100 | 26 | 2000 | 0.75 | 0.10 | -0.0306956 | 0.9880639 | -1.8158644 | 390 |
| 0.99 | 500 | 26 | 2000 | 0.75 | 0.10 | -0.0471728 | 0.9969958 | 0.8033748 | 391 |
| 0.50 | 500 | 26 | 2000 | 0.75 | 0.10 | -0.0306086 | 0.8875994 | -210.8357239 | 392 |
| 0.99 | 5 | 5 | 2000 | 0.75 | 0.10 | -0.0517067 | 0.9609682 | 1.4002295 | 393 |
| 0.50 | 5 | 5 | 2000 | 0.75 | 0.10 | -0.0368165 | 1.0067318 | -0.2139769 | 394 |
| 0.99 | 50 | 5 | 2000 | 0.75 | 0.10 | -0.0523651 | 0.9781168 | 1.9834888 | 395 |
| 0.50 | 50 | 5 | 2000 | 0.75 | 0.10 | -0.0374755 | 0.9907757 | -3.2806295 | 396 |
| 0.99 | 100 | 5 | 2000 | 0.75 | 0.10 | -0.0528524 | 0.9842068 | 1.7880239 | 397 |
| 0.50 | 100 | 5 | 2000 | 0.75 | 0.10 | -0.0374662 | 0.9867604 | -9.0009784 | 398 |
| 0.99 | 500 | 5 | 2000 | 0.75 | 0.10 | -0.0530934 | 1.0083663 | -5.0443234 | 399 |
| 0.50 | 500 | 5 | 2000 | 0.75 | 0.10 | -0.0373194 | 0.9380314 | -212.5361204 | 400 |
| 0.99 | 5 | 26 | 200 | 0.75 | 0.10 | NaN | 1.0172410 | 0.4793721 | 401 |
| 0.50 | 5 | 26 | 200 | 0.75 | 0.10 | -0.0364621 | 1.0302270 | 0.3086134 | 402 |
| 0.99 | 50 | 26 | 200 | 0.75 | 0.10 | NaN | 0.9887807 | 0.9182725 | 403 |
| 0.50 | 50 | 26 | 200 | 0.75 | 0.10 | -0.0367754 | 0.9828443 | -1.5498454 | 404 |
| 0.99 | 100 | 26 | 200 | 0.75 | 0.10 | NaN | 1.0036127 | 0.5535500 | 405 |
| 0.50 | 100 | 26 | 200 | 0.75 | 0.10 | -0.0367499 | 1.0281341 | -2.0492349 | 406 |
| 0.99 | 500 | 26 | 200 | 0.75 | 0.10 | NaN | 0.9674801 | 0.2813486 | 407 |
| 0.50 | 500 | 26 | 200 | 0.75 | 0.10 | -0.0371278 | 0.9934670 | -30.4123237 | 408 |
| 0.99 | 5 | 5 | 200 | 0.75 | 0.10 | -0.0487415 | 0.9961493 | 0.8808842 | 409 |
| 0.50 | 5 | 5 | 200 | 0.75 | 0.10 | -0.0347884 | 1.0557868 | -0.5945568 | 410 |
| 0.99 | 50 | 5 | 200 | 0.75 | 0.10 | -0.0498638 | 0.9882798 | 0.9125207 | 411 |
| 0.50 | 50 | 5 | 200 | 0.75 | 0.10 | -0.0356575 | 1.0209387 | -1.0185270 | 412 |
| 0.99 | 100 | 5 | 200 | 0.75 | 0.10 | -0.0502961 | 1.0099741 | 1.3090573 | 413 |
| 0.50 | 100 | 5 | 200 | 0.75 | 0.10 | -0.0356914 | 1.0339449 | -3.7008863 | 414 |
| 0.99 | 500 | 5 | 200 | 0.75 | 0.10 | -0.0505763 | 1.0153989 | -3.4548909 | 415 |
| 0.50 | 500 | 5 | 200 | 0.75 | 0.10 | -0.0360318 | 0.9854936 | -29.1664808 | 416 |
| 0.99 | 5 | 26 | 4000 | 0.75 | 0.10 | -0.0464872 | 0.9718567 | 0.9646147 | 417 |
| 0.50 | 5 | 26 | 4000 | 0.75 | 0.10 | -0.0296851 | 1.0183350 | 0.3137396 | 418 |
| 0.99 | 50 | 26 | 4000 | 0.75 | 0.10 | -0.0470670 | 0.9962575 | 1.0384250 | 419 |
| 0.50 | 50 | 26 | 4000 | 0.75 | 0.10 | -0.0304232 | 1.0092603 | -0.5815051 | 420 |
| 0.99 | 100 | 26 | 4000 | 0.75 | 0.10 | -0.0474767 | 0.9858920 | 1.2092874 | 421 |
| 0.50 | 100 | 26 | 4000 | 0.75 | 0.10 | -0.0304971 | 1.0070173 | -1.9175854 | 422 |
| 0.99 | 500 | 26 | 4000 | 0.75 | 0.10 | -0.0477459 | 1.0096409 | 0.7343386 | 423 |
| 0.50 | 500 | 26 | 4000 | 0.75 | 0.10 | -0.0305096 | 0.8734271 | -368.6283843 | 424 |
| 0.99 | 5 | 5 | 4000 | 0.75 | 0.10 | -0.0514868 | 0.9702743 | 1.4183611 | 425 |
| 0.50 | 5 | 5 | 4000 | 0.75 | 0.10 | -0.0375952 | 1.0148619 | 0.1569573 | 426 |
| 0.99 | 50 | 5 | 4000 | 0.75 | 0.10 | -0.0522527 | 0.9867444 | 1.8621050 | 427 |
| 0.50 | 50 | 5 | 4000 | 0.75 | 0.10 | -0.0381946 | 1.0142682 | -2.8236435 | 428 |
| 0.99 | 100 | 5 | 4000 | 0.75 | 0.10 | -0.0526159 | 0.9908595 | 1.9960862 | 429 |
| 0.50 | 100 | 5 | 4000 | 0.75 | 0.10 | -0.0383034 | 1.0051731 | -9.1683410 | 430 |
| 0.99 | 500 | 5 | 4000 | 0.75 | 0.10 | -0.0528470 | 1.0260676 | -5.1749553 | 431 |
| 0.50 | 500 | 5 | 4000 | 0.75 | 0.10 | -0.0382155 | 0.9427841 | -367.7469595 | 432 |